MOSformer: Momentum encoder-based inter-slice fusion transformer for medical image segmentation
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866914151644790784 |
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| author | Huang, De-Xing Zhou, Xiao-Hu Gui, Mei-Jiang Xie, Xiao-Liang Liu, Shi-Qi Wang, Shuang-Yi Feng, Zhen-Qiu Lai, Zhi-Chao Hou, Zeng-Guang |
| author_facet | Huang, De-Xing Zhou, Xiao-Hu Gui, Mei-Jiang Xie, Xiao-Liang Liu, Shi-Qi Wang, Shuang-Yi Feng, Zhen-Qiu Lai, Zhi-Chao Hou, Zeng-Guang |
| contents | Medical image segmentation takes an important position in various clinical applications. 2.5D-based segmentation models bridge the computational efficiency of 2D-based models with the spatial perception capabilities of 3D-based models. However, existing 2.5D-based models primarily adopt a single encoder to extract features of target and neighborhood slices, failing to effectively fuse inter-slice information, resulting in suboptimal segmentation performance. In this study, a novel momentum encoder-based inter-slice fusion transformer (MOSformer) is proposed to overcome this issue by leveraging inter-slice information from multi-scale feature maps extracted by different encoders. Specifically, dual encoders are employed to enhance feature distinguishability among different slices. One of the encoders is moving-averaged to maintain consistent slice representations. Moreover, an inter-slice fusion transformer (IF-Trans) module is developed to fuse inter-slice multi-scale features. MOSformer is evaluated on three benchmark datasets (Synapse, ACDC, and AMOS), achieving a new state-of-the-art with 85.63%, 92.19%, and 85.43% DSC, respectively. These results demonstrate MOSformer's competitiveness in medical image segmentation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_11856 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MOSformer: Momentum encoder-based inter-slice fusion transformer for medical image segmentation Huang, De-Xing Zhou, Xiao-Hu Gui, Mei-Jiang Xie, Xiao-Liang Liu, Shi-Qi Wang, Shuang-Yi Feng, Zhen-Qiu Lai, Zhi-Chao Hou, Zeng-Guang Image and Video Processing Computer Vision and Pattern Recognition Medical image segmentation takes an important position in various clinical applications. 2.5D-based segmentation models bridge the computational efficiency of 2D-based models with the spatial perception capabilities of 3D-based models. However, existing 2.5D-based models primarily adopt a single encoder to extract features of target and neighborhood slices, failing to effectively fuse inter-slice information, resulting in suboptimal segmentation performance. In this study, a novel momentum encoder-based inter-slice fusion transformer (MOSformer) is proposed to overcome this issue by leveraging inter-slice information from multi-scale feature maps extracted by different encoders. Specifically, dual encoders are employed to enhance feature distinguishability among different slices. One of the encoders is moving-averaged to maintain consistent slice representations. Moreover, an inter-slice fusion transformer (IF-Trans) module is developed to fuse inter-slice multi-scale features. MOSformer is evaluated on three benchmark datasets (Synapse, ACDC, and AMOS), achieving a new state-of-the-art with 85.63%, 92.19%, and 85.43% DSC, respectively. These results demonstrate MOSformer's competitiveness in medical image segmentation. |
| title | MOSformer: Momentum encoder-based inter-slice fusion transformer for medical image segmentation |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2401.11856 |